This document discusses sampling methods used in research. It defines sampling as obtaining information from a subset of a larger population. The key sections cover the sampling process, types of sampling including probability and non-probability methods, sources of sampling error, and factors to consider when determining sample size such as the nature of the population, number of variables, desired accuracy level, and available finances. Probability methods like simple random and stratified sampling aim to give all population members an equal chance of selection, while non-probability techniques like convenience and snowball sampling do not. Sample size is an important factor in controlling random error.
The document discusses key concepts in sampling, including:
- The target population is the group to which results will be generalized.
- Sampling units are the smallest elements that can be selected from the sampling frame.
- The sampling frame is the list from which potential respondents are drawn.
- Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select a representative sample and allow estimates of sampling error. Non-probability methods do not involve random selection.
This document discusses developing a sample plan, which involves six steps: 1) defining the relevant population, 2) obtaining a population list, 3) designing the sample method and size, 4) drawing the sample, 5) assessing the sample, and 6) resampling if necessary. It also covers basic sampling concepts and different probability and non-probability sampling methods.
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
This document discusses various sampling methods used in business research. It defines key terms like population, element, sample, and parameter. It describes probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It also discusses non-probability sampling techniques including convenience sampling and purposive sampling. Specific methods covered include quota sampling and judgmental sampling. The document provides examples to illustrate each sampling technique.
Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.
This document discusses sampling methods used in research. It outlines key concepts like population, sample, probability sampling, and non-probability sampling. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling and cluster sampling are explained. Non-probability methods like convenience sampling, judgment sampling, and quota sampling are also outlined. Factors to consider for determining sample size and types of errors in sampling are discussed. The advantages and disadvantages of probability and non-probability sampling are compared.
This document discusses various sampling techniques used in research. It begins by defining key terms like population, sample, and sampling unit. It then explains different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. For each method, it provides details on the procedure and highlights advantages and disadvantages. The document aims to help readers understand different sampling designs and how to select appropriate techniques for research studies.
This document discusses sampling methods used in research. It defines sampling as obtaining information from a subset of a larger population. The key sections cover the sampling process, types of sampling including probability and non-probability methods, sources of sampling error, and factors to consider when determining sample size such as the nature of the population, number of variables, desired accuracy level, and available finances. Probability methods like simple random and stratified sampling aim to give all population members an equal chance of selection, while non-probability techniques like convenience and snowball sampling do not. Sample size is an important factor in controlling random error.
The document discusses key concepts in sampling, including:
- The target population is the group to which results will be generalized.
- Sampling units are the smallest elements that can be selected from the sampling frame.
- The sampling frame is the list from which potential respondents are drawn.
- Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling aim to select a representative sample and allow estimates of sampling error. Non-probability methods do not involve random selection.
This document discusses developing a sample plan, which involves six steps: 1) defining the relevant population, 2) obtaining a population list, 3) designing the sample method and size, 4) drawing the sample, 5) assessing the sample, and 6) resampling if necessary. It also covers basic sampling concepts and different probability and non-probability sampling methods.
Research is defined as a systematic, empirical investigation guided by theory to understand natural phenomena. It involves identifying a problem, reviewing existing literature, developing hypotheses and variables, collecting and analyzing data, and drawing conclusions. There are important components to research including the research design, methodology, instrumentation, sampling, data analysis, and conclusions. Sampling involves selecting a subset of a population to study. Probability sampling aims to give all population members an equal chance of selection, while non-probability sampling does not. Common probability sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling.
This document discusses various sampling methods used in business research. It defines key terms like population, element, sample, and parameter. It describes probability sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It also discusses non-probability sampling techniques including convenience sampling and purposive sampling. Specific methods covered include quota sampling and judgmental sampling. The document provides examples to illustrate each sampling technique.
Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.
This document discusses sampling methods used in research. It outlines key concepts like population, sample, probability sampling, and non-probability sampling. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling and cluster sampling are explained. Non-probability methods like convenience sampling, judgment sampling, and quota sampling are also outlined. Factors to consider for determining sample size and types of errors in sampling are discussed. The advantages and disadvantages of probability and non-probability sampling are compared.
This document discusses various sampling techniques used in research. It begins by defining key terms like population, sample, and sampling unit. It then explains different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. For each method, it provides details on the procedure and highlights advantages and disadvantages. The document aims to help readers understand different sampling designs and how to select appropriate techniques for research studies.
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
This document discusses simple random sampling, which is a type of probability sampling technique where each member of the population has an equal chance of being selected. It provides examples to illustrate simple random sampling, such as selecting sugar from a bag or using a lottery system or random number table to randomly pick sample members. The key aspects of simple random sampling are that selection is random and does not depend on the characteristics of the population members, giving each member an equal chance of selection.
1. Sampling is selecting a subset of a population to make inferences about the whole population. It involves defining the population, specifying a sampling frame and sampling unit, choosing a sampling method, determining sample size, and selecting the sample.
2. There are two main types of sampling methods - probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection is unknown. Common probability methods include simple random sampling, systematic sampling, and stratified sampling. Common non-probability methods include quota sampling, snowball sampling, and convenience sampling.
3. Sources of error in sampling include sampling errors, which arise from differences between the sample and population, and non-sampling
The document discusses different types of sampling designs used in research, including probability and non-probability sampling. Probability sampling methods aim to give all members of the population an equal chance of being selected and include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling methods do not use random selection and include convenience sampling, purposive sampling, and quota sampling. The key factors to consider in sampling design are determining the target population, parameters of interest, sampling frame, appropriate sampling method, and sample size.
The document discusses different types of sampling designs used in research. It describes probability sampling methods like simple random sampling and systematic sampling which allow every unit in the population to have a chance of being selected. It also covers non-probability sampling which does not assure equal chance of selection. Key factors in sampling like sample size, target population, and parameters of interest are explained.
Characteristics of a good sample design & types of sample designDr.Sangeetha R
The document discusses different types of sample designs, including their key characteristics and differences. It covers non-probability sampling designs like purposive sampling which rely on researcher judgement, and probability sampling designs like simple random sampling where every item has an equal chance of selection. Probability sampling is preferred because it allows estimating sampling errors and significance of results.
This document discusses different types of sampling methods used in research. It describes probability sampling methods such as simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling techniques including convenience sampling, purposive or judgement sampling, snowball sampling, and quota sampling. The key aspects of each sampling method are defined along with their advantages and disadvantages.
This document discusses sampling from a population. A population includes all items related to an inquiry, while a sample is a representative subset of the population. Simple random sampling (SRS) is the process of drawing a sample from a population where each unit has an equal chance of being selected. There are two types of SRS: with replacement, where selected units can be selected again; and without replacement, where selected units are not returned before selecting the next unit. Random number tables and lottery methods are two common techniques used to select simple random samples from large populations.
The document discusses cluster sampling and multistage sampling methods. Cluster sampling involves splitting the population into clusters, randomly selecting some clusters, and sampling every unit within those clusters. Multistage sampling combines multiple sampling methods, such as stratified and cluster sampling. It is commonly used in surveys conducted by polling organizations. Some advantages of cluster and multistage sampling are that they are simpler and less costly than simple random sampling, while still allowing estimates of population characteristics.
The document discusses stratified random sampling, which is a statistical sampling technique where the population is first divided into homogeneous subgroups or strata, then a random sample is drawn from each stratum. The key steps are to 1) identify and define the population, 2) determine sample size, 3) identify variables and subgroups for representation, 4) classify population members into subgroups, and 5) randomly select an appropriate number of individuals from each subgroup. Stratified random sampling can reduce bias and variability compared to simple random sampling. However, it requires knowing the names of all population members and may be difficult if some selected cannot be reached.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
This document discusses research methodology and sampling techniques. It defines key terms like population, sample, census, and probability and non-probability sampling. It describes different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and their advantages and disadvantages. Finally, it discusses issues around internet sampling and methods like using web site visitors, panels, and opt-in lists.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sampling and sample size in statistics. It defines key terms like population, sample, sampling unit, sampling frame, and sampling schemes. It explains that sampling allows researchers to generalize results from a subset of the population. The main advantages of sampling are that it is less costly, takes less time, and can provide more accurate results than studying the entire population. The document also discusses different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It notes that sample size depends on several factors and must result in a truly representative sample with small errors.
The document discusses sampling design and different types of sampling techniques. It explains that a census collects data from the entire population while a sample survey selects a sample of respondents that is representative of the total population. There are two main types of sampling designs - probability sampling, where each item has a known chance of being selected, and non-probability sampling, which does not use random selection. The key steps in developing a sampling design are determining the target population, sampling unit, sampling frame, sample size, and sampling procedure. Factors like costs and reducing bias and sampling errors must also be considered.
This document provides an overview of sampling, including definitions, purposes, types of sampling, and sources of error. Sampling involves selecting a subset of a population to make inferences about the whole population. It is done for reasons of economy, timeliness, large population size, and inaccessibility. Types of sampling include probability methods like simple random and stratified sampling, and non-probability methods like convenience and purposive sampling. Sources of error include sampling error due to chance and bias, as well as non-sampling error from data collection methods.
The document discusses different sampling techniques used in research. It defines sampling as selecting a representative subset of a population to make inferences about. There are two main types of sampling techniques: probability sampling and non-probability sampling. Probability sampling involves random selection so that every member of the population has an equal chance of being selected. It then describes several probability sampling techniques in detail, including simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multi-stage sampling. For each technique it provides examples and discusses their merits and demerits.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
The document discusses various sampling methods used in research. It defines key terms like population, sampling element, sampling frame, and inference. It then explains probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It discusses their advantages and disadvantages. The document also covers non-probability sampling methods like convenience sampling and purposive sampling. It provides examples of different types of purposive sampling and discusses their advantages over random sampling in certain research objectives.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This document discusses sampling methods used in statistics. It defines sampling as making inferences about a whole population by examining a subset of selected units. The main purposes of sampling are to provide statistical information about a population more efficiently and accurately than a complete census. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to give all population units an equal chance of selection. Non-probability methods like convenience sampling and judgmental sampling do not use random selection. The document also outlines different sampling types and the steps involved in the sampling process.
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
This document discusses simple random sampling, which is a type of probability sampling technique where each member of the population has an equal chance of being selected. It provides examples to illustrate simple random sampling, such as selecting sugar from a bag or using a lottery system or random number table to randomly pick sample members. The key aspects of simple random sampling are that selection is random and does not depend on the characteristics of the population members, giving each member an equal chance of selection.
1. Sampling is selecting a subset of a population to make inferences about the whole population. It involves defining the population, specifying a sampling frame and sampling unit, choosing a sampling method, determining sample size, and selecting the sample.
2. There are two main types of sampling methods - probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection is unknown. Common probability methods include simple random sampling, systematic sampling, and stratified sampling. Common non-probability methods include quota sampling, snowball sampling, and convenience sampling.
3. Sources of error in sampling include sampling errors, which arise from differences between the sample and population, and non-sampling
The document discusses different types of sampling designs used in research, including probability and non-probability sampling. Probability sampling methods aim to give all members of the population an equal chance of being selected and include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling methods do not use random selection and include convenience sampling, purposive sampling, and quota sampling. The key factors to consider in sampling design are determining the target population, parameters of interest, sampling frame, appropriate sampling method, and sample size.
The document discusses different types of sampling designs used in research. It describes probability sampling methods like simple random sampling and systematic sampling which allow every unit in the population to have a chance of being selected. It also covers non-probability sampling which does not assure equal chance of selection. Key factors in sampling like sample size, target population, and parameters of interest are explained.
Characteristics of a good sample design & types of sample designDr.Sangeetha R
The document discusses different types of sample designs, including their key characteristics and differences. It covers non-probability sampling designs like purposive sampling which rely on researcher judgement, and probability sampling designs like simple random sampling where every item has an equal chance of selection. Probability sampling is preferred because it allows estimating sampling errors and significance of results.
This document discusses different types of sampling methods used in research. It describes probability sampling methods such as simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling techniques including convenience sampling, purposive or judgement sampling, snowball sampling, and quota sampling. The key aspects of each sampling method are defined along with their advantages and disadvantages.
This document discusses sampling from a population. A population includes all items related to an inquiry, while a sample is a representative subset of the population. Simple random sampling (SRS) is the process of drawing a sample from a population where each unit has an equal chance of being selected. There are two types of SRS: with replacement, where selected units can be selected again; and without replacement, where selected units are not returned before selecting the next unit. Random number tables and lottery methods are two common techniques used to select simple random samples from large populations.
The document discusses cluster sampling and multistage sampling methods. Cluster sampling involves splitting the population into clusters, randomly selecting some clusters, and sampling every unit within those clusters. Multistage sampling combines multiple sampling methods, such as stratified and cluster sampling. It is commonly used in surveys conducted by polling organizations. Some advantages of cluster and multistage sampling are that they are simpler and less costly than simple random sampling, while still allowing estimates of population characteristics.
The document discusses stratified random sampling, which is a statistical sampling technique where the population is first divided into homogeneous subgroups or strata, then a random sample is drawn from each stratum. The key steps are to 1) identify and define the population, 2) determine sample size, 3) identify variables and subgroups for representation, 4) classify population members into subgroups, and 5) randomly select an appropriate number of individuals from each subgroup. Stratified random sampling can reduce bias and variability compared to simple random sampling. However, it requires knowing the names of all population members and may be difficult if some selected cannot be reached.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
This document discusses research methodology and sampling techniques. It defines key terms like population, sample, census, and probability and non-probability sampling. It describes different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and their advantages and disadvantages. Finally, it discusses issues around internet sampling and methods like using web site visitors, panels, and opt-in lists.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sampling and sample size in statistics. It defines key terms like population, sample, sampling unit, sampling frame, and sampling schemes. It explains that sampling allows researchers to generalize results from a subset of the population. The main advantages of sampling are that it is less costly, takes less time, and can provide more accurate results than studying the entire population. The document also discusses different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It notes that sample size depends on several factors and must result in a truly representative sample with small errors.
The document discusses sampling design and different types of sampling techniques. It explains that a census collects data from the entire population while a sample survey selects a sample of respondents that is representative of the total population. There are two main types of sampling designs - probability sampling, where each item has a known chance of being selected, and non-probability sampling, which does not use random selection. The key steps in developing a sampling design are determining the target population, sampling unit, sampling frame, sample size, and sampling procedure. Factors like costs and reducing bias and sampling errors must also be considered.
This document provides an overview of sampling, including definitions, purposes, types of sampling, and sources of error. Sampling involves selecting a subset of a population to make inferences about the whole population. It is done for reasons of economy, timeliness, large population size, and inaccessibility. Types of sampling include probability methods like simple random and stratified sampling, and non-probability methods like convenience and purposive sampling. Sources of error include sampling error due to chance and bias, as well as non-sampling error from data collection methods.
The document discusses different sampling techniques used in research. It defines sampling as selecting a representative subset of a population to make inferences about. There are two main types of sampling techniques: probability sampling and non-probability sampling. Probability sampling involves random selection so that every member of the population has an equal chance of being selected. It then describes several probability sampling techniques in detail, including simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multi-stage sampling. For each technique it provides examples and discusses their merits and demerits.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
The document discusses various sampling methods used in research. It defines key terms like population, sampling element, sampling frame, and inference. It then explains probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It discusses their advantages and disadvantages. The document also covers non-probability sampling methods like convenience sampling and purposive sampling. It provides examples of different types of purposive sampling and discusses their advantages over random sampling in certain research objectives.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This document discusses sampling methods used in statistics. It defines sampling as making inferences about a whole population by examining a subset of selected units. The main purposes of sampling are to provide statistical information about a population more efficiently and accurately than a complete census. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to give all population units an equal chance of selection. Non-probability methods like convenience sampling and judgmental sampling do not use random selection. The document also outlines different sampling types and the steps involved in the sampling process.
This document discusses various sampling methods used in research. It begins by defining key sampling terms like population, sample, sampling unit, and sampling frame. It then describes the main types of sampling: probability sampling methods which use random selection and allow statistical inference about the population, and non-probability sampling methods which do not use random selection. Specific probability methods discussed include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. Common non-probability methods mentioned are convenience sampling, purposive sampling, and snowball sampling. The document provides details on how to implement several of these sampling techniques and notes their relative advantages and limitations.
In the Pharmaceutical, We can get accurate result of the whole population or Whole Batch only and only if Our Sampling Method is perfect and Accurate.
Sampling is also one of the IMP technique for the Statistical calculations.
Sampling is the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. There are several sampling techniques including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and non-probability sampling. Each technique has advantages and disadvantages related to accuracy, cost, and generalizability. Proper sampling helps reduce sampling errors and increase the reliability of making inferences about the population from a sample.
Sampling methods- Random, Systematic and Snowball Sonalikuril72
The document discusses different sampling techniques used in research, including probability sampling methods like simple random sampling and systematic sampling, as well as non-probability sampling methods like snowball sampling. It provides models and examples of each technique, and discusses their advantages and disadvantages.
This document discusses different types of sampling methods used in qualitative research. It defines key terms like sample, random sampling, and non-probability sampling. It then explains different sampling techniques in more detail, including simple random sampling, systematic random sampling, stratified random sampling, multi-stage cluster sampling, convenience sampling, snowball sampling, quota sampling, accidental sampling, panel sampling, and improving response rates. The document emphasizes that qualitative researchers are more concerned with understanding phenomena in depth than statistical validity or generalizability.
The document discusses sample and sampling techniques used in research. It defines key terms like population, sample, sampling, and element. It describes two main sampling techniques - probability sampling which uses random selection, and non-probability sampling which uses non-random methods. Some examples of probability sampling techniques include simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Examples of non-probability sampling include convenience sampling, quota sampling, and purposive sampling. Sample size is determined using formulas like Slovin's formula.
This document summarizes the key components of a research methodology section, including:
1) Explaining how data was collected and analyzed to obtain results.
2) Justifying the methods used by explaining why they were appropriate for the research objectives and data being collected.
3) Discussing any problems encountered and how they were addressed.
The document discusses methodology sections in research papers. It provides examples of methodology sections and discusses what they should include. It lists things like when and where the research was conducted, the data collection procedures, criteria for including subjects, a description of surveys used to collect data, and how results will be presented. It also includes multiple links to methodology sections from published research papers that could be used as examples.
Systematic sampling is a technique where every nth sample is selected from a list to be included in the overall sample. This is useful when subjects are logically arranged like alphabetically or geographically. The procedure is easy to do manually and results are generally representative of the population unless a characteristic repeats every nth individual. To conduct systematic sampling, a starting number and interval are selected, where the interval is the constant difference between consecutive samples. This sampling method is illustrated through an example where every 8th individual is selected from a population of 100 to obtain a sample of 12.
This document discusses sampling techniques and sample types. It defines key terms like population, sample, element, and sampling unit. It outlines the sampling process which includes defining the population, determining the sampling frame, executing the sampling, and determining sample size. The main types of sampling covered are probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and double sampling as well as non-probability methods like convenience sampling and purposive sampling. Ethical considerations in data collection like informed consent, privacy, and protecting participants are also addressed.
This document discusses anti helminthic drugs used to treat helminth infections. It begins by introducing helminths and the prevalence of helminthiasis globally and in developing countries. It then discusses the classification of helminths and the pharmacological targets of antihelminthic drugs. The document proceeds to describe several classes of antihelminthic drugs in detail, including their mechanisms of action, adverse effects, contraindications, and uses for treating specific helminth infections. Key drugs discussed include mebendazole, albendazole, pyrantel pamoate, diethylcarbamazine, ivermectin, and praziquantel. In the end, the document
This document provides an overview of case study research. It discusses key aspects of case studies such as their use in qualitative research to investigate phenomena in real-life contexts. Case studies allow for an in-depth and holistic examination of situations and can utilize multiple data sources. The document outlines different approaches to case study research by scholars like Yin and Stake and provides examples of possible case study topics. It also addresses issues like research design, data analysis and reporting for case studies.
This document discusses sampling methods used in research. It lists the group members conducting the research and covers topics including probability and non-probability sampling methods. For probability sampling, it describes random sampling, stratified sampling, systematic sampling, and cluster sampling. It provides examples and discusses the advantages and disadvantages of systematic sampling. For non-probability sampling it discusses convenience sampling, judgmental sampling, quota sampling, and snowball sampling, giving examples of when each method would be used. It concludes with a brief definition of sampling size.
1. The document lists objectives and homework reminders for reading chapter 12 and completing problems 24, 26, 28, and 30.
2. It discusses sampling methods including simple random sampling (SRS), where every possible sample has an equal chance of being selected, and stratified sampling, where the population is divided into subgroups.
3. It provides an example of identifying different sampling methods from scenarios and asks the reader to name the sampling method described.
This document discusses concepts related to data sampling and probability. It covers the multiplication rule for probability, conditional probability, types of sampling methods including simple random sampling and stratified sampling, frequency distributions for organizing data, and qualitative versus quantitative data. Key probability formulas are presented for finding conditional probabilities, permutations, and combinations.
The document discusses key concepts related to sampling methods in marketing research. It defines sampling elements, population, sampling frame, and sampling unit. It presents formulas for calculating sample size when estimating means of continuous variables and proportions. The formula for means involves variables like confidence level (Z), standard deviation (s), and tolerable error (e). The formula for proportions uses variables like confidence level (Z), estimated proportion (p), and tolerable error (e). The document provides an example of each formula and discusses limitations of the formulas related to number of centers, multiple questions, and cell size in analysis.
This document discusses different sampling methods used in research. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. For each method it covers the key aspects, strengths, and weaknesses. The goal of sampling is to select a subset of a population that represents the whole population to make inferences about.
This document provides an overview of different sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods, like convenience sampling. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The document aims to help readers understand why sampling is necessary, different sampling techniques, and how to select the appropriate method for their research needs.
This document provides an overview of different sampling methods used in research. It begins by defining research and the key components of empirical research. It then defines sampling and why researchers sample rather than studying entire populations. The document discusses and provides examples of different probability sampling methods, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also covers non-probability sampling methods. For each method, it highlights the advantages and disadvantages. The goal is to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples and how to select the appropriate sampling method for their research needs.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to drawing sample populations and how to select the most appropriate method for their research needs.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and the relative advantages and disadvantages. The goal is to help readers understand different approaches to collecting samples and how to select the most appropriate method for their research needs.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key points are that sampling allows researchers to make inferences about a population while using fewer subjects, and the type of sampling method impacts the accuracy and potential for bias in the results.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It distinguishes between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods. For each method, it discusses how the sample is selected and their relative advantages and disadvantages. The goal is to help readers understand different approaches to drawing sample populations and how to select the most appropriate method for their research needs.
This document provides an overview of sampling methods for research. It begins by defining research and the key components of empirical research. It then defines sampling as selecting a subset of a population to make inferences about that population. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. It also discusses non-probability sampling and compares different sampling techniques. The document aims to help readers understand sampling frames, sample sizes, and how to select appropriate sampling methods for research studies.
This document provides an overview of sampling methods for research. It defines key terms like population, sample, and sampling frame. It also explains different types of sampling techniques including probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability sampling methods like convenience sampling are also discussed. The document compares advantages and disadvantages of different sampling approaches. It provides examples of how to implement certain sampling designs in practice.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and advantages and disadvantages. Cluster sampling is also explained as a multi-stage process where clusters rather than individuals are selected.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and advantages and disadvantages. Cluster sampling is also explained as a multi-stage process where clusters rather than individuals are selected.
This document discusses various sampling methods used in research. It begins by defining a sample and explaining why sampling is used instead of surveying entire populations. The document then distinguishes between probability sampling methods, which assign a known probability of selection to each unit, and non-probability sampling methods, which do not. Specific probability methods covered include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Non-probability methods discussed are convenience sampling and purposive sampling. Advantages and disadvantages of each approach are provided.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, and non-probability sampling methods. For each method, it provides details on how the sampling is conducted and notes advantages and disadvantages. The goal is to help readers understand different approaches to collecting representative samples from a population in a way that allows results to be generalized.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then distinguishes between probability sampling methods, like simple random sampling, systematic sampling, and stratified sampling, which assign a known probability of selection to units, and non-probability sampling methods, which do not. The document provides details on how to implement different probability sampling techniques and discusses their relative advantages and disadvantages. It emphasizes that the goal of sampling is to select a subset of a population that is representative of the whole.
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of units from a larger population. The document then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes how to implement the technique and highlights advantages and disadvantages. The key goal is to help readers understand how to appropriately select samples to gather data about a target population.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
This document discusses different sampling methods used in research. It begins by defining sampling as selecting a subset of a population to make inferences about the whole population. The document then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. Key advantages and disadvantages of each method are described.
This document discusses various sampling methods used in research. It begins by defining key terms like population, sample, and sampling frame. It then covers different types of sampling, distinguishing between probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling, and non-probability sampling methods like convenience sampling. For each method, it provides details on how to implement it and notes advantages and disadvantages. The document aims to help readers understand different sampling techniques and how to select the appropriate method for their research needs.
PRODUCTION AND OPERATIONS
MANAGEMENT
-Management function responsible for producing goods & services
-Objectives of production management
-Functions of production management
-Production system & models
-Research
Research is an art of scientific investigation.
It is regarded as a systematic efforts to gain new knowledge.
The dictionary meaning of research is “a careful investigation or enquiry especially through search for new facts in any branch of knowledge”.
-Nature of Research
-Objectives of Research
-Types of Research
-Research Process
-Significance of Research
-Criteria for Good Research
-Limitations of Research
Organizational Culture
A common perception held by the organization’s members; a system of shared meaning.
Characteristics:
Innovation and risk taking
Attention to detail
Outcome orientation
People orientation
Team orientation
Aggressiveness
Stability
Do Organizations Have Uniform Cultures?
What Is Organizational Culture? (cont’d)
What Do Cultures Do?
How Culture Begins?
Keeping Culture Alive
Stages in the Socialization Process
How Employees Learn Culture
Creating An Ethical Organizational Culture
Creating a Customer-Responsive Culture..
JOHARI WINDOW
A MODEL of self awareness , personal development, group development and understanding relationship
The Johari Window model was developed by American psychologists Joseph Luft and Harry Ingham in the 1950's
Interestingly, Luft and Ingham called their Johari Window model 'Johari' after combining their first names, Joe and Harry.
In early publications the word actually appears as 'JoHari
The Johari Window model is also referred to as a 'disclosure/feedback model of self awareness', and by some people an 'information processing tool'.
The Johari Window soon became a widely used model for understanding and training self-awareness, personal development, improving communications, interpersonal relationships, group dynamics, team development and inter-group relationships.
Refers to others and self
-Others – other people in the team
-Oneself the person subject to johari window analysis
The Johari Window actually represents information - feelings, experience, views, attitudes, skills, intentions, motivation, etc - within or about a person - in relation to their group, from four perspectives, which are described below
JOHARI WINDOW – 4 REGIONS
-Open Area -what is known by the person about him/herself and is also known by others - open self, free area, free self, or 'the arena'
-Blind Area - what is unknown by the person about him/herself but which others know - blind area, blind self, or 'blind spot'
-Hidden Area - what the person knows about him/herself that others do not know - hidden area, hidden self, avoided area, avoided self or 'facade'
-Unknown Area -what is unknown by the person about him/herself and is also unknown by others - unknown area or unknown self .
Inventory includes raw materials, parts, work-in-process, and finished goods held in the supply chain. Managing inventory well can increase sales, reduce costs, and boost profits. At the firm level, poor inventory management led companies like Solectron and Palm to suffer large losses from excess inventory during downturns. The economic order quantity (EOQ) model helps determine the optimal order size by balancing ordering and holding costs. Safety stock is added to the reorder point to account for uncertain demand and achieve a target service level.
-GROUPS:
-Types of groups
-Stages of group development
-Why do people join groups
-Group Dynamics
-Factors influencing group working
-Group Behavior
-Group member Resources
-Group Structure
-Group Process
-Group Tasks.
This document provides an overview of key concepts related to formulating and testing hypotheses. It defines a hypothesis as a proposition or claim about a population that can be empirically tested. Hypothesis testing involves examining two opposing hypotheses: the null hypothesis (H0) and alternative hypothesis (Ha). It describes the basic steps of hypothesis testing as formulating the hypotheses, defining a test statistic, determining the distribution of the test statistic, defining the critical region, and making a decision to accept or reject the null hypothesis. Key concepts like type I and type II errors, significance levels, critical values, and one-tailed vs two-tailed tests are also explained. Parametric tests like the z-test, t-test, and
-WHAT ARE ECOSYSTEMS?
-Parts of an Ecosystem
-Different types of organisms live in an ecosystem.
-Community
-Habitat
-Kinds Of Ecosystem
-Types of Ecosystems
-Components of Ecosystem
-Functions of an ecosystem
-PROCESSES OF ECOSYSTEMS
-Energy Flow Chart
-Types of Food Chains (Samples)
-Food Web
-Ecological Pyramids
-Types of Ecological Pyramids
-Industrial Ecology and Recycling Industry
-Recycling
-Environmental management system (EMS)
-ISO 14000
-Objectives of ISO 14000
-How are these standards developed?
-The 17 requirements of the ISO 14001
-Other standards in ISO 14001 series
Cost Accounting-
-Meaning of Cost Accounting
-Scope of Cost Accounting
-Nature of Cost Accounting
-Relationship b/w Financial Accounting & Cost Accounting
-Cost Accounting v/s Management Accounting
-Objectives of cost accounting
-Function of cost accountant
-Essentials of cost accounting
-Advantages of cost accounting
-Limitations of cost accounting
-Role of cost in cost accounting
-Cost Unit & Cost Centre
-Cost Techniques
-Costing Systems
-Costing Methods
-Cost Classification
-Components of total cost
-Cost Sheet.
ORGANISATIONAL CHANGE & STRESS MANAGEMENT
-Managing Planned change
-Resistance to change
-Overcoming resistance to change
-Politics of change
-Lewin's Three Step Change Model
-Action Research
-Organisational Development
-OD Techniques
-Change issues for today's Managers
Technology in workplace
Stimulating Innovation
Creating & managing a learning organisation
Culture-Bond in organisation
-Work Stress & its management
-Types of stress
-Demand-Resources Model of Stress
-Potential Sources of Stress
-Consequences of Stress
-Not all Stress is Bad
-Burnout
-Stress v/s Burnout
-Managing stress
-Global Implications
-Summary & Managerial Implications
-How to Manage stress.
This document summarizes a presentation on organizational conflicts. It defines conflict as a state of opposition between groups that can result from different ideas, goals or structures. Conflicts are viewed as natural and can have both positive and negative impacts. Conflicts can occur at the individual, group and organizational levels. The conflict process involves potential incompatibility, personalization of issues, behavioral intentions, overt conflict behaviors, and outcomes. Effective conflict management aims to improve situations and strengthen relationships through collaborative solutions.
Presentation on Budget, Budgeting & Budgetary control
Contents:
1) Budgeting [characteristics]
2) Budgetary control
3) Difference in budget, budgeting, budgetary control
4) Essentials in budgetary control
5) Requisites for budgetary control system
6) Merits & limitations
7) Zero-based budgeting
8) Difference in Traditional & Zero based budgeting.
Presentation on Budget, budgeting and budgetary control..
Contents-
1) Budgeting [characteristics]
2) Budgetary control
3) Difference in budget, budgeting, budgetary control
4) Essentials in budgetary control
5) Requisites for budgetary control system
6) Merits & limitations
7) Zero-based budgeting
8) Difference in Traditional & Zero based budgeting.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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2. LEARNING OBJECTIVES
2
Learn the reasons for sampling
Develop an understanding about different
sampling methods
Distinguish between probability & non probability
sampling
Discuss the relative advantages & disadvantages
of each sampling methods
3. What is research?
3
• “Scientific research is systematic, controlled,
empirical, and critical investigation of natural
phenomena guided by theory and hypotheses
about the presumed relations among such
phenomena.”
– Kerlinger, 1986
• Research is an organized and systematic way of
finding answers to questions
4. Important Components of Empirical Research
4
Problem statement, research questions, purposes,
benefits
Theory, assumptions, background literature
Variables and hypotheses
Operational definitions and measurement
Research design and methodology
Instrumentation, sampling
Data analysis
Conclusions, interpretations, recommendations
5. SAMPLING
5
A sample is “a smaller (but hopefully
representative) collection of units from a
population used to determine truths about that
population” (Field, 2005)
Why sample?
Resources (time, money) and workload
Gives results with known accuracy that can be
calculated mathematically
The sampling frame is the list from which the
potential respondents are drawn
Registrar’s office
Class rosters
Must assess sampling frame errors
6. SAMPLING……
6
What is your population of interest?
To whom do you want to generalize your
results?
All doctors
School children
Indians
Women aged 15-45 years
Other
Can you sample the entire population?
7. SAMPLING…….
7
3 factors that influence sample representative-
ness
Sampling procedure
Sample size
Participation (response)
When might you sample the entire population?
When your population is very small
When you have extensive resources
When you don’t expect a very high response
10. Types of Samples
10
Probability (Random) Samples
Simple random sample
Systematic random sample
Stratified random sample
Multistage sample
Multiphase sample
Cluster sample
Non-Probability Samples
Convenience sample
Purposive sample
Quota
11. Process
11
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or
events possible to measure
Specifying a sampling method for selecting
items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process
12. Population definition
12
A population can be defined as including all
people or items with the characteristic one
wishes to understand.
Because there is very rarely enough time or
money to gather information from everyone
or everything in a population, the goal
becomes finding a representative sample (or
subset) of that population.
13. Population definition…….
13
Note also that the population from which the sample
is drawn may not be the same as the population about
which we actually want information. Often there is
large but not complete overlap between these two
groups due to frame issues etc .
Sometimes they may be entirely separate - for
instance, we might study rats in order to get a
better understanding of human health, or we might
study records from people born in 2008 in order to
make predictions about people born in 2009.
14. SAMPLING FRAME
14
In the most straightforward case, such as the
sentencing of a batch of material from production
(acceptance sampling by lots), it is possible to
identify and measure every single item in the
population and to include any one of them in our
sample. However, in the more general case this is not
possible. There is no way to identify all rats in the
set of all rats. Where voting is not compulsory, there
is no way to identify which people will actually vote at
a forthcoming election (in advance of the election)
As a remedy, we seek a sampling frame which has the
property that we can identify every single element
and include any in our sample .
The sampling frame must be representative of the
population
15. PROBABILITY SAMPLING
15
A probability sampling scheme is one in which every
unit in the population has a chance (greater than zero)
of being selected in the sample, and this probability
can be accurately determined.
. When every element in the population does have the
same probability of selection, this is known as an
'equal probability of selection' (EPS) design. Such
designs are also referred to as 'self-weighting'
because all sampled units are given the same weight.
16. PROBABILITY SAMPLING…….
16
Probability sampling includes:
Simple Random Sampling,
Systematic Sampling,
Stratified Random Sampling,
Cluster Sampling
Multistage Sampling.
Multiphase sampling
17. NON PROBABILITY SAMPLING
17
Any sampling method where some elements of population
have no chance of selection (these are sometimes
referred to as 'out of coverage'/'undercovered'), or
where the probability of selection can't be accurately
determined. It involves the selection of elements based
on assumptions regarding the population of interest, which
forms the criteria for selection. Hence, because the
selection of elements is nonrandom, nonprobability
sampling not allows the estimation of sampling errors..
Example: We visit every household in a given street, and
interview the first person to answer the door. In any
household with more than one occupant, this is a
nonprobability sample, because some people are more
likely to answer the door (e.g. an unemployed person who
spends most of their time at home is more likely to
answer than an employed housemate who might be at work
when the interviewer calls) and it's not practical to
calculate these probabilities.
18. NONPROBABILITY SAMPLING…….
18
• Nonprobability Sampling includes:
Accidental Sampling, Quota Sampling and
Purposive Sampling. In addition, nonresponse
effects may turn any probability design into a
nonprobability design if the characteristics of
nonresponse are not well understood, since
nonresponse effectively modifies each
element's probability of being sampled.
19. SIMPLE RANDOM SAMPLING
19
• Applicable when population is small, homogeneous &
readily available
• All subsets of the frame are given an equal
probability. Each element of the frame thus has an
equal probability of selection.
• It provides for greatest number of possible samples.
This is done by assigning a number to each unit in the
sampling frame.
• A table of random number or lottery system is used
to determine which units are to be selected.
20. SIMPLE RANDOM SAMPLING……..
20
Estimates are easy to calculate.
Simple random sampling is always an EPS design, but not all
EPS designs are simple random sampling.
Disadvantages
If sampling frame large, this method impracticable.
Minority subgroups of interest in population may not be
present in sample in sufficient numbers for study.
21. REPLACEMENT OF SELECTED UNITS
21
Sampling schemes may be without replacement
('WOR' - no element can be selected more than once
in the same sample) or with replacement ('WR' - an
element may appear multiple times in the one
sample).
For example, if we catch fish, measure them, and
immediately return them to the water before
continuing with the sample, this is a WR design,
because we might end up catching and measuring the
same fish more than once. However, if we do not
return the fish to the water (e.g. if we eat the fish),
this becomes a WOR design.
22. SYSTEMATIC SAMPLING
22
Systematic sampling relies on arranging the target
population according to some ordering scheme and then
selecting elements at regular intervals through that
ordered list.
Systematic sampling involves a random start and then
proceeds with the selection of every kth element from
then onwards. In this case, k=(population size/sample
size).
It is important that the starting point is not
automatically the first in the list, but is instead
randomly chosen from within the first to the kth
element in the list.
A simple example would be to select every 10th name
from the telephone directory (an 'every 10th' sample,
also referred to as 'sampling with a skip of 10').
23. SYSTEMATIC SAMPLING……
23
As described above, systematic sampling is an EPS method, because all
elements have the same probability of selection (in the example
given, one in ten). It is not 'simple random sampling' because
different subsets of the same size have different selection
probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten
probability of selection, but the set {4,13,24,34,...} has zero
probability of selection.
24. SYSTEMATIC SAMPLING……
24
ADVANTAGES:
Sample easy to select
Suitable sampling frame can be identified easily
Sample evenly spread over entire reference population
DISADVANTAGES:
Sample may be biased if hidden periodicity in population
coincides with that of selection.
Difficult to assess precision of estimate from one survey.
25. STRATIFIED SAMPLING
25
Where population embraces a number of distinct
categories, the frame can be organized into separate
"strata." Each stratum is then sampled as an
independent sub-population, out of which individual
elements can be randomly selected.
Every unit in a stratum has same chance of being
selected.
Using same sampling fraction for all strata ensures
proportionate representation in the sample.
Adequate representation of minority subgroups of
interest can be ensured by stratification & varying
sampling fraction between strata as required.
26. STRATIFIED SAMPLING……
26
Finally, since each stratum is treated as an
independent population, different sampling
approaches can be applied to different strata.
Drawbacks to using stratified sampling.
First, sampling frame of entire population has to
be prepared separately for each stratum
Second, when examining multiple criteria,
stratifying variables may be related to some, but
not to others, further complicating the design,
and potentially reducing the utility of the strata.
Finally, in some cases (such as designs with a
large number of strata, or those with a specified
minimum sample size per group), stratified
sampling can potentially require a larger sample
than would other methods
28. POSTSTRATIFICATION
28
Stratification is sometimes introduced after the
sampling phase in a process called "poststratification“.
This approach is typically implemented due to a lack of
prior knowledge of an appropriate stratifying variable
or when the experimenter lacks the necessary
information to create a stratifying variable during the
sampling phase. Although the method is susceptible to
the pitfalls of post hoc approaches, it can provide
several benefits in the right situation. Implementation
usually follows a simple random sample. In addition to
allowing for stratification on an ancillary variable,
poststratification can be used to implement weighting,
which can improve the precision of a sample's
estimates.
29. OVERSAMPLING
29
Choice-based sampling is one of the stratified
sampling strategies. In this, data are
stratified on the target and a sample is taken
from each strata so that the rare target class
will be more represented in the sample. The
model is then built on this biased sample. The
effects of the input variables on the target
are often estimated with more precision with
the choice-based sample even when a smaller
overall sample size is taken, compared to a
random sample. The results usually must be
adjusted to correct for the oversampling.
30. CLUSTER SAMPLING
30
Cluster sampling is an example of 'two-stage
sampling' .
First stage a sample of areas is chosen;
Second stage a sample of respondents within
those areas is selected.
Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
Sampling units are groups rather than individuals.
A sample of such clusters is then selected.
All units from the selected clusters are studied.
31. CLUSTER SAMPLING…….
31
Advantages :
Cuts down on the cost of preparing a sampling
frame.
This can reduce travel and other
administrative costs.
Disadvantages: sampling error is higher for a
simple random sample of same size.
Often used to evaluate vaccination coverage in
EPI
32. CLUSTER SAMPLING…….
32
• Identification of clusters
– List all cities, towns, villages & wards of cities with
their population falling in target area under study.
– Calculate cumulative population & divide by 30, this
gives sampling interval.
– Select a random no. less than or equal to sampling
interval having same no. of digits. This forms 1st
cluster.
– Random no.+ sampling interval = population of 2nd
cluster.
– Second cluster + sampling interval = 4th
cluster.
– Last or 30th
cluster = 29th
cluster + sampling interval
33. CLUSTER SAMPLING…….
33
Two types of cluster sampling methods.
One-stage sampling. All of the elements within
selected clusters are included in the sample.
Two-stage sampling. A subset of elements
within selected clusters are randomly selected
for inclusion in the sample.
34. CLUSTER SAMPLING…….
34
• Freq c f cluster
• I 2000 2000 1
• II 3000 5000 2
• III 1500 6500
• IV 4000 10500 3
• V 5000 15500 4, 5
• VI 2500 18000 6
• VII 2000 20000 7
• VIII 3000 23000 8
• IX 3500 26500 9
• X 4500 31000 10
• XI 4000 35000 11, 12
• XII 4000 39000 13
• XIII 3500 44000 14,15
• XIV 2000 46000
• XV 3000 49000 16
• XVI 3500 52500 17
• XVII 4000 56500 18,19
• XVIII 4500 61000 20
• XIX 4000 65000 21,22
• XX 4000 69000 23
• XXI 2000 71000 24
• XXII 2000 73000
• XXIII 3000 76000 25
• XXIV 3000 79000 26
• XXV 5000 84000 27,28
• XXVI 2000 86000 29
• XXVII 1000 87000
• XXVIII 1000 88000
• XXIX 1000 89000 30
• XXX 1000 90000
• 90000/30 = 3000 sampling interval
35. Difference Between Strata and Clusters
35
Although strata and clusters are both non-
overlapping subsets of the population, they
differ in several ways.
All strata are represented in the sample; but
only a subset of clusters are in the sample.
With stratified sampling, the best survey
results occur when elements within strata are
internally homogeneous. However, with cluster
sampling, the best results occur when elements
within clusters are internally heterogeneous
36. MULTISTAGE SAMPLING
36
Complex form of cluster sampling in which two or more levels of
units are embedded one in the other.
First stage, random number of districts chosen in all
states.
Followed by random number of talukas, villages.
Then third stage units will be houses.
All ultimate units (houses, for instance) selected at last step are
surveyed.
37. MULTISTAGE SAMPLING……..
37
This technique, is essentially the process of taking random
samples of preceding random samples.
Not as effective as true random sampling, but probably
solves more of the problems inherent to random sampling.
An effective strategy because it banks on multiple
randomizations. As such, extremely useful.
Multistage sampling used frequently when a complete list
of all members of the population not exists and is
inappropriate.
Moreover, by avoiding the use of all sample units in all
selected clusters, multistage sampling avoids the large,
and perhaps unnecessary, costs associated with traditional
cluster sampling.
38. MULTI PHASE SAMPLING
38
Part of the information collected from whole sample & part from
subsample.
In Tb survey MT in all cases – Phase I
X –Ray chest in MT +ve cases – Phase II
Sputum examination in X – Ray +ve cases - Phase III
Survey by such procedure is less costly, less laborious & more
purposeful
39. MATCHED RANDOM SAMPLING
39
A method of assigning participants to groups in which
pairs of participants are first matched on some
characteristic and then individually assigned randomly to
groups.
The Procedure for Matched random sampling can be
briefed with the following contexts,
Two samples in which the members are clearly paired, or
are matched explicitly by the researcher. For example,
IQ measurements or pairs of identical twins.
Those samples in which the same attribute, or variable, is
measured twice on each subject, under different
circumstances. Commonly called repeated measures.
Examples include the times of a group of athletes for
1500m before and after a week of special training; the
milk yields of cows before and after being fed a
particular diet.
40. QUOTA SAMPLING
40
The population is first segmented into mutually exclusive
sub-groups, just as in stratified sampling.
Then judgment used to select subjects or units from
each segment based on a specified proportion.
For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of
non-probability sampling.
In quota sampling the selection of the sample is non-
random.
For example interviewers might be tempted to interview
those who look most helpful. The problem is that these
samples may be biased because not everyone gets a
chance of selection. This random element is its greatest
weakness and quota versus probability has been a matter
of controversy for many years
41. CONVENIENCE SAMPLING
41
Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
A type of nonprobability sampling which involves the sample being
drawn from that part of the population which is close to hand.
That is, readily available and convenient.
The researcher using such a sample cannot scientifically make
generalizations about the total population from this sample
because it would not be representative enough.
For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the people
that he/she could interview would be limited to those given there
at that given time, which would not represent the views of other
members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
This type of sampling is most useful for pilot testing.
In social science research, snowball sampling is a similar technique,
where existing study subjects are used to recruit more subjects
into the sample.
43. Judgmental sampling or Purposive
sampling
43
- The researcher chooses the sample based on
who they think would be appropriate for the
study. This is used primarily when there is a
limited number of people that have expertise
in the area being researched
44. PANEL SAMPLING
44
Method of first selecting a group of participants through a
random sampling method and then asking that group for the same
information again several times over a period of time.
Therefore, each participant is given same survey or interview at
two or more time points; each period of data collection called a
"wave".
This sampling methodology often chosen for large scale or nation-
wide studies in order to gauge changes in the population with
regard to any number of variables from chronic illness to job
stress to weekly food expenditures.
Panel sampling can also be used to inform researchers about
within-person health changes due to age or help explain changes in
continuous dependent variables such as spousal interaction.
There have been several proposed methods of analyzing panel
sample data, including growth curves.
46. What sampling method u recommend?
46
Determining proportion of undernourished five year
olds in a village.
Investigating nutritional status of preschool children.
Selecting maternity records for the study of previous
abortions or duration of postnatal stay.
In estimation of immunization coverage in a province,
data on seven children aged 12-23 months in 30
clusters are used to determine proportion of fully
immunized children in the province.
Give reasons why cluster sampling is used in this
survey.
47. Probability proportional to size
sampling
47
In some cases the sample designer has access to an "auxiliary variable"
or "size measure", believed to be correlated to the variable of
interest, for each element in the population. This data can be used to
improve accuracy in sample design. One option is to use the auxiliary
variable as a basis for stratification, as discussed above.
Another option is probability-proportional-to-size ('PPS') sampling,
in which the selection probability for each element is set to be
proportional to its size measure, up to a maximum of 1. In a simple
PPS design, these selection probabilities can then be used as the basis
for Poisson sampling. However, this has the drawbacks of variable
sample size, and different portions of the population may still be
over- or under-represented due to chance variation in selections. To
address this problem, PPS may be combined with a systematic
approach.
48. Contd.
48
Example: Suppose we have six schools with populations of 150,
180, 200, 220, 260, and 490 students respectively (total 1500
students), and we want to use student population as the basis for a
PPS sample of size three. To do this, we could allocate the first
school numbers 1 to 150, the second school 151 to
330 (= 150 + 180), the third school 331 to 530, and so on to the
last school (1011 to 1500). We then generate a random start
between 1 and 500 (equal to 1500/3) and count through the school
populations by multiples of 500. If our random start was 137, we
would select the schools which have been allocated numbers 137,
637, and 1137, i.e. the first, fourth, and sixth schools.
The PPS approach can improve accuracy for a given sample size by
concentrating sample on large elements that have the greatest
impact on population estimates. PPS sampling is commonly used
for surveys of businesses, where element size varies greatly and
auxiliary information is often available - for instance, a survey
attempting to measure the number of guest-nights spent in hotels
might use each hotel's number of rooms as an auxiliary variable. In
some cases, an older measurement of the variable of interest can be
used as an auxiliary variable when attempting to produce more
current estimates.
49. Event sampling
49
Event Sampling Methodology (ESM) is a new form of
sampling method that allows researchers to study ongoing
experiences and events that vary across and within days in its
naturally-occurring environment. Because of the frequent
sampling of events inherent in ESM, it enables researchers to
measure the typology of activity and detect the temporal and
dynamic fluctuations of work experiences. Popularity of ESM as a
new form of research design increased over the recent years
because it addresses the shortcomings of cross-sectional research,
where once unable to, researchers can now detect intra-individual
variances across time. In ESM, participants are asked to record
their experiences and perceptions in a paper or electronic diary.
There are three types of ESM:# Signal contingent – random
beeping notifies participants to record data. The advantage of this
type of ESM is minimization of recall bias.
Event contingent – records data when certain events occur
50. Contd.
50
Event contingent – records data when certain events occur
Interval contingent – records data according to the passing of a
certain period of time
ESM has several disadvantages. One of the disadvantages of ESM is
it can sometimes be perceived as invasive and intrusive by
participants. ESM also leads to possible self-selection bias. It may
be that only certain types of individuals are willing to participate
in this type of study creating a non-random sample. Another
concern is related to participant cooperation. Participants may not
be actually fill out their diaries at the specified times.
Furthermore, ESM may substantively change the phenomenon
being studied. Reactivity or priming effects may occur, such that
repeated measurement may cause changes in the participants'
experiences. This method of sampling data is also highly
vulnerable to common method variance.[6]
51. contd.
51
Further, it is important to think about whether or not an
appropriate dependent variable is being used in an ESM design.
For example, it might be logical to use ESM in order to answer
research questions which involve dependent variables with a great
deal of variation throughout the day. Thus, variables such as
change in mood, change in stress level, or the immediate impact
of particular events may be best studied using ESM methodology.
However, it is not likely that utilizing ESM will yield meaningful
predictions when measuring someone performing a repetitive task
throughout the day or when dependent variables are long-term in
nature (coronary heart problems).
Editor's Notes
PROBLEM STATEMENT, PURPOSES, BENEFITS
What exactly do I want to find out?
What is a researchable problem?
What are the obstacles in terms of knowledge, data availability, time, or resources?
Do the benefits outweigh the costs?
THEORY, ASSUMPTIONS, BACKGROUND LITERATURE
What does the relevant literature in the field indicate about this problem?
Which theory or conceptual framework does the work fit within?
What are the criticisms of this approach, or how does it constrain the research process?
What do I know for certain about this area?
What is the background to the problem that needs to be made available in reporting the work?
VARIABLES AND HYPOTHESES
What will I take as given in the environment ie what is the starting point?
Which are the independent and which are the dependent variables?
Are there control variables?
Is the hypothesis specific enough to be researchable yet still meaningful?
How certain am I of the relationship(s) between variables?
OPERATIONAL DEFINITIONS AND MEASUREMENT
Does the problem need scoping/simplifying to make it achievable?
What and how will the variables be measured?
What degree of error in the findings is tolerable?
Is the approach defendable?
RESEARCH DESIGN AND METHODOLOGY
What is my overall strategy for doing this research?
Will this design permit me to answer the research question?
What constraints will the approach place on the work?
INSTRUMENTATION/SAMPLING
How will I get the data I need to test my hypothesis?
What tools or devices will I use to make or record observations?
Are valid and reliable instruments available, or must I construct my own?
How will I choose the sample?
Am I interested in representativeness?
If so, of whom or what, and with what degree of accuracy or level of confidence?
DATA ANALYSIS
What combinations of analytical and statistical process will be applied to the data?
Which of these will allow me to accept or reject my hypotheses?
Do the findings show numerical differences, and are those differences important?
CONCLUSIONS, INTERPRETATIONS, RECOMMENDATIONS
Was my initial hypothesis supported?
What if my findings are negative?
What are the implications of my findings for the theory base, for the background assumptions, or relevant literature?
What recommendations result from the work?
What suggestions can I make for further research on this topic?
Sampling frame errors: university versus personal email addresses; changing class rosters; are all students in your population of interest represented?
How do we determine our population of interest?
Administrators can tell us
We notice anecdotally or through qualitative research that a particular subgroup of students is experiencing higher risk
We decide to do everyone and go from there
3 factors that influence sample representativeness
Sampling procedure
Sample size
Participation (response)
When might you sample the entire population?
When your population is very small
When you have extensive resources
When you don’t expect a very high response
Picture of sampling breakdown
Two general approaches to sampling are used in social science research. With probability sampling, all elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. With nonprobability sampling, in contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of nonprobability sample is called a convenience sample – not because such samples are necessarily easy to recruit, but because the researcher uses whatever individuals are available rather than selecting from the entire population.
Because some members of the population have no chance of being sampled, the extent to which a convenience sample – regardless of its size – actually represents the entire population cannot be known